A Hidden Markov Model Based Segmentation Framework for MR Spectroscopy Imaging

Information

  • Research Project
  • 7107639
  • ApplicationId
    7107639
  • Core Project Number
    R41EB005520
  • Full Project Number
    1R41EB005520-01A1
  • Serial Number
    5520
  • FOA Number
  • Sub Project Id
  • Project Start Date
    7/1/2006 - 18 years ago
  • Project End Date
    9/30/2007 - 17 years ago
  • Program Officer Name
    SWAIN, AMY L
  • Budget Start Date
    7/1/2006 - 18 years ago
  • Budget End Date
    9/30/2007 - 17 years ago
  • Fiscal Year
    2006
  • Support Year
    1
  • Suffix
    A1
  • Award Notice Date
    6/30/2006 - 18 years ago
Organizations

A Hidden Markov Model Based Segmentation Framework for MR Spectroscopy Imaging

[unreadable] DESCRIPTION (provided by applicant): The convergence of biomedicine and computation in the form of biomedical computing has demonstrated gains in the areas of genetic sequences, biomedical images, qualitative descriptors for health and social science and geospatial images and chemical formulae. This provides a unique opportunity to utilize novel image processing techniques to more accurately and objectively characterize anatomical and molecular structures and establish tractable measurements and quantification of normal and disease states. Accurate quantification of brain metabolites from Magnetic Resonance Spectroscopic Imaging (MRSI) is becoming increasingly important in the examination of long-term effects of disease and monitoring of the effects of treatment in cancer, neuro-degenerative diseases, and mental health. During Phase I, this proposal will develop an innovative image segmentation framework for the analysis of MRSI data, which is accurate, robust, and computationally efficient for eventual use as a tool in monitoring cancer treatment. The proposed framework is based on a novel utilization of Hidden Markov Models (HMM) that are traind to recognize different tissue parameters in the brain and is then used for the segmentation of MR data. The HMM-based segmentation is used for its attractive accuracy, robustness and computational efficiency (as tradeoff with accuracy) characteristics which are demonstrated from the mathematical foundation of the HMM as well as the preliminary results. The segmentation framework will then be used as part of a system to provide reproducible and tractable quantification of brain metabolites in MRSI analysis for cancer treatment analysis. Based on the success of Phase I, in Phase II, the tools developed from the framework will be integrated within a Picture Archiving and Communication Systems (PACS) for clinical use utilizing MRSI datasets for monitoring the effects of cancer treatment. Accurate in vivo quantification of brain metabolites is useful in examination of long-term effects of disease and monitoring the effects of treatment. This provides a unique opportunity to utilize novel image processing techniques to more accurately and objectively characterize anatomical and molecular structures and establish tractable measurements and quantification of normal and disease states. Co-analysis of segmented MR imaging data and "functional" MR data can improve the accuracy of assessing the burden of disease in patients with neurodegenerative, inflammatory/infectious, and neurovascular disorders. [unreadable] [unreadable] [unreadable]

IC Name
NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING
  • Activity
    R41
  • Administering IC
    EB
  • Application Type
    1
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    291121
  • Sub Project Total Cost
  • ARRA Funded
  • CFDA Code
    375
  • Ed Inst. Type
  • Funding ICs
    NCMHD:228761\NIBIB:62360\
  • Funding Mechanism
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    INFOTECH SOFT, INC.
  • Organization Department
  • Organization DUNS
    035354070
  • Organization City
    MIAMI
  • Organization State
    FL
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    33131
  • Organization District
    UNITED STATES